Secure Data Warehousing through Access Control: Critical Challenges, Limitations, and Future Perspectives
Keywords:
ABAC, Access control mechanisms, Adaptive access control, DAC, Data warehouse security, Hybrid access control, Machine Learning (ML), RBAC, Security challengesAbstract
Critical review of data warehouses that store large volumes of sensitive, heterogeneous, and business-critical information, making effective access control essential for maintaining confidentiality, integrity, and availability. Traditional access control mechanisms, including Discretionary Access Control, Mandatory Access Control, Role-Based Access Control, and Attribute-Based Access Control, have been widely adopted to regulate access to organizational data. The increasing scale, complexity, distribution, and dynamic nature of modern data warehouse environments have exposed several limitations in conventional approaches. This paper presents a critical analysis of traditional access control mechanisms with particular emphasis on their security limitations, operational challenges, and emerging requirements. The study examines their effectiveness in addressing dynamic user behavior, fine-grained authorization, evolving access patterns, insider threats, privilege escalation, scalability, and context-aware access requirements. The findings indicate that although traditional mechanisms provide structured and policy-driven authorization, they often rely on predefined rules and static configurations, limiting their ability to respond effectively to continuously changing access behaviors and emerging security threats. The analysis further highlights challenges related to policy management complexity, adaptability, real-time decision-making, and behavioral anomaly detection. Based on these findings, the paper identifies emerging requirements for more adaptive, intelligent, context-aware, and behavior-sensitive access control mechanisms.
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